A Promising Genetic Algorithm Approach to Job-Shop SchedulingRe-Schedulingand Open-Shop Scheduling Problems
نویسندگان
چکیده
The general job-shop scheduling problem is known to be extremely hard. We describe a GA approach which produces reasonably good results very quickly on standard benchmark job-shop scheduling problems, better than previous eeorts using genetic algorithms for this task, and comparable to existing conventional search-based methods. The representation used is a variant of one known to work moderately well for the traveling salesman problem. It has the considerable merit that crossover will always produce legal schedules. A novel method for performance enhancement is examined based on dynamic sampling of the convergence rates in diierent parts of the genome. Our approach also promises to eeectively address the open-shop scheduling problem and the job-shop rescheduling problem.
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